UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings

• URL: http://arxiv.org/abs/2502.16961v1
• Date: Mon, 24 Feb 2025 08:38:21 GMT
• Title: UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings
• Authors: Layba Fiaz, Munief Hassan Tahir, Sana Shams, Sarmad Hussain,
• Abstract summary: This paper introduces UrduLLaMA 1.0, a model derived from the open-source Llama-3.1-8B-Instruct architecture.<n>We leverage Low-Rank Adaptation (LoRA) to fine tune the model on 41,000 Urdu instructions and approximately 50,000 English-Urdu translation pairs.
• Score: 0.7874708385247353
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multilingual Large Language Models (LLMs) often provide suboptimal performance on low-resource languages like Urdu. This paper introduces UrduLLaMA 1.0, a model derived from the open-source Llama-3.1-8B-Instruct architecture and continually pre-trained on 128 million Urdu tokens, capturing the rich diversity of the language. To enhance instruction-following and translation capabilities, we leverage Low-Rank Adaptation (LoRA) to fine tune the model on 41,000 Urdu instructions and approximately 50,000 English-Urdu translation pairs. Evaluation across three machine translation datasets demonstrates significant performance improvements compared to state-of-the-art (SOTA) models, establishing a new benchmark for Urdu LLMs. These findings underscore the potential of targeted adaptation strategies with limited data and computational resources to address the unique challenges of low-resource languages.

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